Analyzing Patterns of Scientific Collaboration using Centrality Measures and Modularity

Modularity is generally used as a measure for determining goodness of clustering. In this paper, we performed analysis on coauthorship network. The authors belong to the field of Biology related to different research and academic institutes from all over the world. We took different centrality measures such as betweenness, degree, page rank, closeness and ranked authors on the basis of these measures. We also found cohesive groups of authors and detected communities in the form of clusters, using modularity metrics such as edge betweenness, walk-trap and label propagation. Our results indicate that different modularity metrics identified communities near to each other and clustered the authors of highly collaborating